Temporomandibular disorders (TMD) are common in dentistry, with complex etiologies and diverse clinical manifestations. Imaging techniques allow visualization and precise quantification of temporomandibular joint (TMJ) structures. MRI can reveal soft tissue abnormalities by analyzing disc morphology, degree of displacement, joint effusion, and lateral pterygoid muscle attachment patterns; while CT provides quantitative parameters of bony structures such as condylar morphology, size, and position. This review summarizes the associations between CT and MRI structural features of the TMJ with TMD severity and prognosis, aiming to provide a theoretical basis for precision diagnosis and treatment of TMD.

Neurovascular coupling (NVC) refers to the brain's regulatory capacity to adjust cerebral blood flow in response to neuronal activity, representing a coordinated function between neural activity and hemodynamics. Impairment in NVC diminishes connectivity between distinct brain regions, thereby impairing cognitive function. Currently, various neuroimaging techniques including arterial spin labeling, resting-state functional MRI, and functional near-infrared spectroscopy are employed to evaluate NVC function. These methods have confirmed that declines in NVC are closely linked to cognitive impairments in conditions such as vascular cognitive impairment, Alzheimer's disease, diabetic cognitive decline, and cognitive deficits in end-stage renal disease patients. This review aims to provide an overview of recent advances in neuroimaging research on the relationship between NVC and cognitive disorders.

Objective To evaluate the value of CT features of pulmonary ground-glass nodules (GGNs) in predicting the invasiveness and invasion degree of lung adenocarcinoma. Methods A retrospective study was conducted on 168 postoperative patients with isolated GGN on chest CT and complete pathological results from surgery or biopsy. Based on whether the lesion had invasive components, patients were divided into a non-invasive group (44 cases) and an invasive group (124 cases). The invasive group was further subdivided into minimally invasive adenocarcinoma group (MIA group, 61 cases) and invasive adenocarcinoma group (IAC group, 63 cases) according to the degree of invasion. CT features of the GGNs were analyzed, including long diameter, short diameter, mean CT value, and proportion of ground-glass opacity (GGO). The intraclass correlation coefficient (ICC) was used to assess interobserver agreement between two radiologists. Independent sample t-tests, Mann-Whitney U tests, and chi-square tests were used to compare CT features between groups. CT features with statistically significant differences were included in multivariate logistic regression analysis to identify independent predictors of invasiveness and invasion degree. Receiver operating characteristic (ROC) curves were used to analyze the predictive performance of independent and combined predictors. Results The measurement consistency measurements of GGN long diameter, short diameter, GGO proportion, and mean CT value between the two physicians was good (all ICC>0.9). Significant differences in the presence of spiculation, lobulation, vascular change, pleural retraction, shape, GGN type, long diameter, short diameter, mean CT value, and GGO proportion were observed between the non-invasive and invasive groups (all P<0.05). Among these, spiculation, mixed GGN (mGGN), and long diameter were independent risk factors for predicting GGN invasiveness (all P<0.05). Significant differences in lobulation, vascular change, vacuole sign, density uniformity, long diameter, short diameter, mean CT value, and GGO proportion were found between the MIA and IAC groups (all P<0.05). Among these, type Ⅱ vascular change, GGN long diameter, short diameter, and mean CT value were independent risk factors for predicting the degree of invasion (all P<0.05). Among single predictors of invasiveness, long diameter had the highest AUC (0.818); the combined predictor model had an AUC of 0.885, higher than any single predictor. For invasion degree prediction, short diameter had the highest AUC (0.896); the combined predictor model had an AUC of 0.945, again higher than any single factor. Conclusion A combined assessment of multiple CT imaging features of GGNs can improve the prediction of both the invasiveness and degree of invasion in lung adenocarcinoma, providing stronger radiological evidence for individualized clinical diagnosis and treatment planning.

Musculoskeletal disorders are characterized by diverse pathological types and complex anatomical structures, placing high demands on the precision of imaging examinations. In recent years, artificial intelligence (AI) has shown great potential in musculoskeletal imaging, particularly in anatomical segmentation, lesion detection, quantitative measurement, and intelligent diagnosis. This review systematically summarizes advances in AI applications for degenerative joint diseases, sports injuries, fracture detection, osteoporosis screening, and musculoskeletal tumors, while also outlining its expanding roles in image reconstruction, quality control, and educational support. Furthermore, it highlights the developmental trends of large AI models in musculoskeletal imaging and discusses their potential and challenges in multimodal, multitask, and personalized clinical decision support. Although AI has reached or even surpassed expert-level performance in certain tasks, limitations remain in model generalization, data acquisition and annotation standards, cross-modality integration, and clinical adaptability. Future progress will require high-quality data construction, interdisciplinary collaboration, and the establishment of standardized frameworks to advance musculoskeletal AI toward more intelligent, efficient, and standardized clinical practice.

Ischemia and hypoxia caused by cerebral vascular stenosis or occlusion constitute the core pathological mechanisms of ischemic stroke. As an important compensatory pathway for blood supply in the body, collateral circulation directly influences the survival time of the ischemic penumbra, the rate of infarct expansion, and the long-term prognosis of patients. This article reviews and analyzes the classification of cerebral collateral circulation and its related hemodynamic basis, CT and MRI-based evaluation of collateral circulation, as well as the pathological basis of collateral circulation in ischemic stroke and its correlation with imaging findings. It is anticipated that future imaging evaluation of collateral circulation will develop toward greater precision, multimodality integration, and intelligence, thereby providing stronger support for individualized patient treatment.

Vulnerable carotid plaques and cerebral small vessel disease (CSVD) are both significant etiologies of ischemic stroke, and they are closely interrelated. Imaging modalities, including ultrasound, CT angiography (CTA), and MR high-resolution vessel wall imaging (HR-VWI), enable the assessment of various vulnerable plaque characteristics. This review focuses on the correlation between vulnerable plaques and CSVD, aiming to provide a theoretical basis for further research and clinical application.

The imaging diagnosis of focal fat-containing hepatic poses a challenge due to the heterogeneity of fat components within the lesions and the overlap of imaging features. This article systematically analyzed the imaging differences among steatosis, adipose tissue, and liquid fat. It summarized the imaging features of focal fat-containing liver lesions such as hepatocellular carcinoma, hepatocellular adenoma, and angiomyolipoma. By identifying the specific type of fat within the lesion, accurately assessing other imaging features (e.g., enhancement patterns), and integrating relevant clinical information, the differential diagnosis can be narrowed, thereby improving diagnostic accuracy.

Pancreatic ductal adenocarcinoma (PDAC) is characterized by insidious early symptoms, rapid progression, and extremely poor prognosis. The Chinese Guidelines for the Diagnosis and Treatment of Pancreatic Cancer (2022 Edition) emphasize the importance of early screening and precision imaging assessment in high-risk populations, and recommend MRI as an important modality for screening and diagnosis. In conjunction with the epidemiological characteristics of PDAC in China and the key points of domestic and international expert consensus, this article systematically interprets the latest international MRI screening protocols and reporting templates. The aim is to improve the early detection rate of PDAC, improve patient prognosis, and provide evidence-based support for the continuous refinement of domestic guidelines.

Objective To investigate a model based on MRI habitat imaging analysis of axillary lymph node (ALN) heterogeneity combined with clinicopathological features for assessing the response to neoadjuvant therapy (NAT) in breast cancer patients with ALN positivity (ALN+). Methods A total of 369 female patients with pathologically confirmed ALN+ breast cancer were retrospectively enrolled and randomly divided into a training set (n=259) and a validation set (n=110) in a 7∶3 ratio. All patients underwent dynamic contrast-enhanced MRI before treatment. Stable 3D radiomic features were used, and the optimal number of clusters was determined using Gaussian mixture modeling and the Bayesian information criterion to generate habitat imaging and extract subregional features. Four support vector machine (SVM)-based models were constructed: a clinical model, a habitat radiomics model, an ALN heterogeneity score model, and a late-fusion combined model. Predictive performance was evaluated using receiver operating characteristic (ROC) curve analysis, and the area under the curve (AUC) values were compared using the Delong test. Clinical utility was assessed using decision curve analysis. Results The clinical model was constructed based on PR status, HER2 status, and Ki-67 index. The habitat radiomics model was developed using 17 non-zero radiomic features extracted from four subregions. An ALN heterogeneity score model and a combined model were also established. In the validation set, the AUCs of the clinical model, habitat radiomics model, ALN heterogeneity score model, and combined model were 0.79, 0.60, 0.69, 0.85, respectively. The Delong test showed that the AUC of the combined model was significantly higher than those of all individual models (all P<0.05). Decision curve analysis demonstrated that the combined model consistently provided a high net benefit within the threshold probability range of 0.2-0.8, indicating favorable clinical applicability. Conclusion A combined model integrating MRI-based habitat radiomics features, ALN heterogeneity scores, and clinicopathological characteristics may assist in evaluating post-NAT lymph node status and support individualized clinical decision-making.

Gastric cancer is one of the malignancies with high incidence and mortality worldwide. Neoadjuvant immunotherapy has significantly improved the prognosis patients with gastric cancer, and it is of great clinical significance to accurately evaluate the efficacy of neoadjuvant immunotherapy at an early stage. Conventional imaging modalities, such as CT and MRI, have demonstrated value in evaluating the efficacy of neoadjuvant immunotherapy. In addition, artificial intelligence models can be widely applied to predict responses to neoadjuvant immunotherapy in gastric cancer and have shown good performance. This article reviews the research progress in the evaluation of neoadjuvant immunotherapy efficacy using conventional imaging modalities and artificial intelligence models in gastric cancer.

Transcranial magnetic stimulation (TMS) is a non-invasive physical therapy technique that has demonstrated clear efficacy in treating various neurological diseases. However, its therapeutic efficacy varies significantly between individuals, and precise localization of personalized stimulation targets is critical to improving efficacy. Structural MRI can guide TMS by accounting for individual differences in brain anatomy. Functional MRI can identify individualized coordinates of brain function and abnormal brain activity, providing personalized stimulation targets for TMS. Resting-state fMRI functional connectivity is believed to provide a “bridge”, enabling magnetic stimulation to propagate from the cortical surface to deep effect targets. This article introduces the progress of MRI in the precise localization of TMS targets.

Hepatocellular carcinoma (HCC) is a common malignant tumor, and early diagnosis and treatment are crucial. Contrast-enhanced ultrasound (CEUS) enables real-time observation and quantitative analysis of blood perfusion in lesion tissues, which helps improve the diagnostic accuracy and the ability to differentiate between HCC subtypes. It also provides effective support for personalized treatment. At present, CEUS has been gradually applied in various aspects of HCC management, including the diagnosis of different subtypes, personalized treatment planning, evaluation of therapeutic efficacy and prognosis, and molecular targeted diagnosis and treatment. This article reviews the current status and recent progress in the application of CEUS in the diagnosis of HCC subtypes and personalized treatment.

Objective To evaluate the setup accuracy of surface-guided radiotherapy (SGRT) in hypofractionated radiotherapy for breast cancer, to quantify the relationship between setup errors and target dose distribution, and to determine setup error control thresholds according to different planning target volume (PTV) sizes. Methods Sixty female patients with breast cancer who underwent breast-conserving surgery and completed hypofractionated radiotherapy were retrospectively enrolled. According to the treatment guidance method, patients were divided into a conventional setup group (n=30) and an SGRT group (n=30). Setup errors between the two groups were compared using the Mann-Whitney U test. Spearman correlation analysis was performed to assess the association between SGRT-derived setup errors and cone-beam computed tomography (CBCT) registration errors, followed by linear fitting using ordinary least squares regression. Dose variations caused by simulated setup errors were evaluated using the Eclipse treatment planning system. Pearson correlation analysis was applied to investigate the linear relationship between PTV volume and actual delivered dose under different setup error conditions. Results Setup errors along the X, Y, and Z axes in the SGRT group were significantly smaller than those in the conventional group (all P<0.05), and the three-dimensional setup errors of all SGRT cases were <0.4 cm. SGRT-derived setup errors showed a strong positive correlation with CBCT-based registration results (r=0.81-0.91, all P<0.001), demonstrating high consistency between the two methods. Dosimetric simulation revealed that when the overall setup error exceeded 0.4 cm, target dose deviations increased markedly, with the Z-axis showing the highest sensitivity. Significant axial differences were observed in the linear correlation between PTV volume and delivered dose. For the Z-axis, a moderate correlation was identified at setup errors of 0.4 cm and ±0.5 cm, and a weak correlation at -0.4 cm. For the X-axis, a weak correlation was observed at -0.4 cm and -0.5 cm, whereas no significant linear correlation was found for the Y-axis at any setup error level. Notably, in patients with PTV <400 cm⊃3;, the Z-axis setup error should be controlled within 0.2 cm. Conclusion SGRT provides accurate, safe, and non-invasive guidance for hypofractionated radiotherapy in breast cancer, reducing radiation exposure and patient psychological burden while improving workflow efficiency. Setup error thresholds along the Z-axis should be individualized based on PTV volume to ensure optimal dose delivery.

Rheumatic diseases (RD) are a group of systemic disorders affecting multiple systems, with chronic inflammatory responses in vascular and connective tissues as their main pathological basis. Due to its sensitive detection of metabolic activity and systemic inflammatory burden, ¹⁸F-FDG PET/CT, has become increasingly important for early diagnosis, assessment of disease activity, and monitoring of treatment in RD. This article reviews the research progress of ¹⁸F-FDG PET/CT in various RD, including rheumatoid arthritis, polymyalgia rheumatica, idiopathic inflammatory myopathies, adult-onset Still’s disease, systemic lupus erythematosus, IgG4-related disease, relapsing polychondritis, Sjögren’s syndrome, spondyloarthritis, and Takayasu arteritis,etc.

Metal implants have been widely used in various surgical procedures. These metal implants generate artifacts during CT examinations, which can compromise the assessment of surrounding tissues such as muscles and bones. In clinical practice, various reconstruction and post-processing techniques are commonly employed to reduce the impact of metal artifacts on lesion evaluation. In recent years, photon-counting computed tomography (PCCT), with its technical characteristics such as high spatial resolution and multi-energy bin capability, has demonstrated distinct advantages in metal artifact reduction. This paper briefly outlines the principles and advantages of PCCT, introduces the main metal artifact reduction techniques currently available, and reviews the recent progress of its clinical applications in the musculoskeletal system, cardiovascular and cerebrovascular system, head and neck, and nervous system.

Ultrafast real-time MRI is a magnetic resonance imaging technique capable of monitoring dynamic physiological processes in real time, while offering both high image quality and high temporal resolution. This technique can depict the dynamic movement of contents at the esophagogastric junction, enable monitoring of reflux events and hiatal hernia, and improve diagnostic accuracy for gastroesophageal reflux disease when combined with morphological parameters of the esophagogastric junction. Additionally, it shows clinical potential in preoperative planning for hiatal hernia repair and in postoperative follow-up after fundoplication. Ultrafast real-time MRI provides a novel adjunctive diagnostic approach for gastroesophageal reflux disease. This review summarizes the recent advances in the application of ultrafast real-time MRI in gastroesophageal reflux disease.

Objective To compare the clinical value of true electrocardiogram (ECG)-gating versus virtual ECG-gating in coronary computed tomography angiography (CCTA). Methods A total of 108 patients with suspected coronary artery disease undergoing CCTA were prospectively enrolled. Because switching between the two gating modes required additional time, participants were alternately assigned by the examination date to the true ECG-gating group (n=54) and the virtual ECG-gating group (n=54). Scans were performed on a United Imaging uCT 960+ wide-detector CT. Examination time and radiation dose were documented. Using the American Heart Association 18-segment model, 5-point subjective image quality scores were obtained, and coronary artery CT attenuation values, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured. For patients who underwent invasive coronary angiography (ICA) within 30 days after CCTA, diagnostic concordance was assessed with ICA as the gold standard, with subgroup analysis by scanning mode. Continuous variables were compared using the t-test or Mann-Whitney U test, and categorical variables with the chi-square test. Receiver operating characteristic (ROC) curve analysis was used to evaluate diagnostic performance. Results In the virtual ECG-gating group, the distal left circumflex artery (LCX) CT attenuation, SNR, and CNR, as well as the subjective image quality score of the posterior descending branch of the right coronary artery, were superior to the true-gating group (all P<0.05). No statistically significant differences were found for other image quality parameters between groups (all P>0.05). Compared with the true-gating group, the virtual ECG-gating group had a 20.5% higher effective dose [3.99 (3.77, 4.28) mSv vs. 3.31 (2.91, 3.75) mSv, P<0.001], a 24.7% shorter total examination time [(3.62±0.34) min vs. (4.81±0.54) min, P<0.05], and a 61% shorter positioning time [(0.76±0.19) min vs. (1.95±0.36) min, P<0.05], but a 14.6% longer image reconstruction time [(48.06±7.18) s vs. (41.93±8.90) s, P<0.05]. In the ICA-validated subgroup, diagnostic concordance rates were 79.17% in the true ECG-gating group and 92.86% in the virtual ECG-gating group; the difference in diagnostic performance was not statistically significant (AUC: 0.792 vs. 0.854, P>0.05). Conclusions Virtual ECG-gated CCTA significantly improves efficiency while maintaining image quality and diagnostic performance, with a slight increase in radiation dose. It is recommended for time-sensitive clinical scenarios, with adherence to dose-optimization principles.

The occurrence, progression, and metastasis of breast cancer are closely associated with the tumor microenvironment. Habitat imaging (HI) can characterize the tumor microenvironment by reflecting intratumoral heterogeneity (ITH), and it holds significant value in assisting the evaluation of molecular characteristics, subtype differentiation, predicting tumor treatment response, assessing metastasis risk, and prognostic prediction in breast cancer. This article briefly explains the principles and methods of HI, reviews the research progress of HI in breast cancer, and analyzes current challenges and future directions, aiming to provide references for the precise diagnosis and personalized treatment of breast cancer.

Objective To investigate the predictive value of quantitatively assessing perirenal and abdominal fat using the mDixon-Quant technique for early renal function changes in patients with type 2 diabetes mellitus (T2DM). Methods Seventy-five patients with T2DM (mean age, 49.65±13.45 years) were prospectively enrolled. According to the urinary albumin creatinine ratio, patients were divided into a normal albuminuria (NAU) group (n=44) and a microalbuminuria (MAU) group (n=31). In addition, 41 age- and sex-matched healthy volunteers were enrolled as a control group. All subjects underwent abdominal mDixon-Quant MRI scanning. Perirenal fat thickness (PRFT) of both kidneys, visceral fat area (VFA), and subcutaneous fat area (SFA) were measured. Independent sample t-test and Mann-Whitney U test were used for comparison between the two groups, while one-way analysis of variance, Kruskal-Wallis test, and chi-square test were used for comparison among three groups. Logistic regression analysis was applied to identify factors associated with early diabetic renal function changes. Pearson or Spearman correlation analyses were used to assess the relationships between total PRFT and clinical characteristics. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the performance of total PRFT, and the area under the curve (AUC) was calculated. Results Compared with the control group, body weight, body mass index (BMI), systolic blood pressure, diastolic blood pressure, VFA, SFA, L-PRFT, R-PRFT, and total PRFT were significantly increased in both the NAU group and the MAU group (all P<0.05). Compared with the NAU group, the MAU groups showed significantly lower estimated glomerular filtration rate (eGFR) and diastolic blood pressure (both P<0.05), while the proportion of males, systolic blood pressure, serum creatinine (SCr), and urea-to-creatinine ratio (UCR) were significantly higher (all P<0.05). Multifactorial logistic regression analysis showed that diastolic blood pressure and total PRFT were independent associated with NAU status in patients with T2DM (P<0.05), and total PRFT remained an independent factor after adjusting diastolic blood pressure (P<0.05). Total PRFT was also an independent associated with MAU status in patients with T2DM (P<0.05). Body weight and BMI were positively correlated with total PRFT (r=0.642 and 0.616, respectively, both P<0.001). ROC curve analysis demonstrated that total PRFT had good predictive performance for NAU and MAU status, with AUC values of 0.822 and 0.810, respectively. Conclusions The mDixon-Quant technique enables noninvasive and quantitative assessment of perirenal and abdominal fat. Perirenal fat, in particular, shows predictive value for early renal function changes in patients with type 2 diabetes mellitus.

Objective To develop a nomogram based on subjective features from contrast-enhanced CT and to evaluate its ability to predict the metastatic risk of enlarged regional lymph nodes after neoadjuvant immunochemotherapy (nICT) in patients with esophageal squamous cell carcinoma (ESCC). Methods This retrospective multicenter study enrolled 60 patients with locally advanced ESCC who underwent nICT followed by surgical resection at three medical centers. A total of 81 radiologically enlarged regional lymph nodes identified on post-nICT CT scans with matched pathological results were analyzed. Long-axis diameter (LAD), short-axis diameter (SAD), and multiple radiologic features of lymph nodes were evaluated. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of nodal metastasis and to construct a nomogram. Model performance was assessed using receiver operating characteristic (ROC) curves and the area under the curve (AUC), with DeLong’s test used for comparisons. Internal validation was conducted using bootstrap resampling. Model discrimination, calibration, and clinical utility were evaluated using the concordance index (C-index), calibration curves, and decision curve analysis (DCA), respectively. Results Multivariate analysis identified a smaller percentage change in LAD (ΔLAD), the presence of radiologic extranodal extension (iENE), and loss of kidney-shaped morphology as independent predictors of lymph node metastasis. The nomogram demonstrated excellent predictive performance, with a C-index of 0.903 and a bootstrap-corrected C-index of 0.893. The AUC of the nomogram was 0.903, with a sensitivity of 90.0% and specificity of 78.7%, significantly outperforming any single imaging feature alone (all P<0.05). Calibration curves showed good agreement between predicted probabilities and observed outcomes. DCA indicated that the combined model provided a higher net benefit across a wide range of threshold probabilities (0.10-0.75). In addition, a ΔLAD cutoff value greater than 27% effectively differentiated reactive nodal enlargement from metastatic nodes, with a sensitivity of 75.0% and specificity of 63.9%. Conclusion The CT-based nomogram developed in this study enables reliable preoperative prediction of metastatic status in enlarged regional lymph nodes after nICT in ESCC patients. This model may serve as a practical imaging tool to distinguish immune-related pseudoprogression from true nodal metastasis and to facilitate individualized treatment planning.

Prostate cancer (PCa) is the most common malignant tumor of the male reproductive system. Biochemical recurrence (BCR) after radical treatment remains the first sign of invasive disease such as PCa. In recent years, artificial intelligence (AI) technologies, particularly radiomics and deep learning, have demonstrated unique advantages and promising applications in medical image analysis. This article reviews the fundamental concepts of BCR in PCa, along with recent advances in AI-based approaches using multiparametric MRI to predict BCR and assess prognosis following radical treatment.

Bladder cancer is a urogenital system malignancy with high incidence and mortality. With the rapid development of artificial intelligence(AI), AI technologies based on machine learning and deep learning have been applied to the imaging diagnosis of bladder cancer, demonstrating potential clinical value particularly in automatic image segmentation, clinical staging, pathological diagnosis, prediction of relevant protein expression, assessment of chemotherapy efficacy, and prognosis evaluation. This article reviews the research progress of AI in the imaging evaluation of bladder cancer.

Objective To explore the injury patterns resulting from different injury mechanisms of the first metatarsophalangeal joint (MTPJ) and to characterize their magnetic resonance imaging (MRI) features, thereby providing accurate imaging evidence for early diagnosis and treatment. Methods This retrospective study included 84 patients (84 feet) with first MTPJ injuries confirmed by surgery or clinical follow-up, with a mean age of 39.8 ± 9.5 years. All patients underwent MR examinations. Injury patterns were analyzed according to the underlying injury mechanisms, and corresponding MRI features were evaluated. Interobserver agreement between two musculoskeletal radiologists for the diagnosis of first MTPJ injuries was assessed using Kappa statistics. Results Based on the injury mechanism, 68 patients sustained plantar plate injuries caused by isolated hyperextension, 6 patients had medial collateral ligament injuries resulting from isolated valgus stress, 7 patients had lateral collateral ligament injuries caused by isolated varus stress, and 3 patients sustained combined valgus and hyperextension injuries. On MRI, injuries involving the central portion of the plantar plate, intersesamoid ligament, metatarsosesamoid ligament, main collateral ligament, accessory sesamoid ligament, and partial-thickness tears of the sesamophalangeal ligament were characterized by irregular morphology and focal hyperintensity on proton density-weighted fat-suppressed images. Full-thickness tears of the sesamophalangeal ligament were manifested as complete disruption of ligament continuity, with the tear extending through the entire ligament. Interobserver agreement for the diagnosis of first MTPJ injuries was good to excellent (all kappa>0.7). Conclusion MRI allows clear visualization of injury patterns and characteristic imaging findings of first MTPJ injuries and enables accurate classification of injury types. It plays an important role in the early diagnosis and precise management of first MTPJ injuries.

Carotid atherosclerotic disease is one of the primary causes of ischemic stroke. Artificial intelligence-based quantitative plaque analysis using computed tomography angiography (CTA) enables automated segmentation and quantification of plaque components, facilitating early prevention and management of ischemic stroke. This article reviews the principle and key parameters of CTA-based quantitative plaque analysis, the detection and segmentation of carotid plaques, and focuses on the application value of this technology in the diagnosis and treatment of carotid atherosclerosis, including plaque quantification and comprehensive assessment of hemodynamic environment, identification of vulnerable plaques, prediction of ischemic stroke recurrence risk, and monitoring and evaluation of treatment efficacy. The limitations of CTA-based quantitative plaque analysis and potential future developments are also discussed.

Objective To analyze the ultrasonographic features of intramuscular lipoma and various subtypes of liposarcoma and assess their value in differential diagnosis. Methods All of 66 patients with pathologically confirmed adipocytic tumors located in trunk or limb muscles were retrospectively analyzed. According to pathological results, patients were divided into a lipoma group (45 cases) and a liposarcoma group (21 cases). The tumor’s shape, boundary, internal echo, degree of echo uniformity, presence of hypoechoic/anechoic areas, fine septations and blood flow signal grading were evaluated. The maximum diameter of tumor was recorded. Differences in clinical data and ultrasound features between the two groups were compared using the Mann-Whitney U test and chi-square test. Ultrasound parameters with statistical significance were further analyzed using receiver operating characteristic (ROC) curve to determine the area under the curve (AUC), sensitivity, and specificity.The ultrasonic characteristics of different liposarcoma subtypes were also analyzed. Results Compared with the lipoma group, the liposarcoma group had a significantly higher incidence of hypoechoic/anechoic areas, fine septations, and grade Ⅰ-Ⅱ blood flow signals, and showed larger maximum diameters (all P<0.05). Blood flow signal grading demonstrated the best diagnostic efficacy (AUC=0.837) for distinguishing lipoma from liposarcoma, while the maximum diameter achieved the highest sensitivity (0.90), and the the presence of hypoechoic/anechoic area yielded the highest specificity (0.91). Ultrasonographically, well-differentiated liposarcoma often showed fine septations and sparse vascularity. Myxoid liposarcomas were characterized by heterogeneous internal echogenicity, with hypoechoic/anechoic areas and relatively abundant blood flow. Undifferentiated liposarcoma typically presented with hyperechoic and hypoechoic, with clear boundaries and relatively rich blood flow signals. Pleomorphic liposarcoma showed mixed echogenicity and relatively abundant blood flow. Conclusion Muscularis lipoma and liposarcoma have different ultrasonic characteristics, such as the maximum diameter, the presence of hypoechoic/anechoic region, the presence of fine septations, and the blood flow signal, which have high diagnostic value in differentiating between lipomas and liposarcomas. Different subtypes of liposarcoma exhibit distinct ultrasonic manifestations.

Objective To explore the value of multiparametric MRI (mpMRI) in detecting clinically significant prostate cancer (csPCa) in patients with biopsy Gleason score 6 prostate cancer. Methods A retrospective study was conducted including 110 patients with biopsy-confirmed prostate cancer and a Gleason score of 6. The mean age was 72.3±7.2 years. All patients underwent mpMRI prior to prostate biopsy, and lesions were assessed using the Prostate Imaging Reporting and Data System (PI-RADS) version 2.1. Postoperative pathological findings served as the reference standard for csPCa, defined according to the Epstein criteria. Patients were divided into csPCa and non-csPCa groups. Clinical and imaging variables were compared using the Mann-Whitney U test, chi-square test, or Fisher’s exact test, as appropriate. Variables showing statistical significance were entered into multivariate logistic regression analysis to identify independent predictors of csPCa. Receiver operating characteristic (ROC) curve analysis was performed to evaluate diagnostic performance, and the area under the curve (AUC) was calculated. Results Among the 110 patients, 92 (83.6%) were confirmed to have csPCa on postoperative pathology. Compared with the non-csPCa group, patients in the csPCa group had significantly larger tumor diameters and a higher proportion of tumor stage ≥T₃ and PI-RADS scores ≥4 (all P<0.05). Multivariate logistic regression analysis identified tumor diameter as the only independent predictor of csPCa (P<0.05). ROC analysis demonstrated that tumor diameter showed the highest diagnostic performance (AUC=0.90), followed by PI-RADS v2.1 score (AUC=0.75) and tumor stage ≥T₃ (AUC=0.63). Tumor diameter achieved the highest sensitivity (95.65%), accuracy (92.52%), and negative predictive value (73.33%), whereas tumor stage ≥T₃ demonstrated the highest specificity and positive predictive value (both 100%). Conclusion mpMRI is valuable for detecting potential csPCa in patients with biopsy Gleason score 6 prostate cancer.